We propose a novel GAN architecture called Reconstruction Global-Local GAN (Recon-GLGAN). The idea is inspired from a GAN based work [1]} in the context of image inpainting. The idea behind Recon-GLGAN is to capture both the global and local contextual features. Recon-GLGAN consists of a generator and a context discriminator. The generator (G) tries to learn the mapping between data distribution of ZF image \(x_{u}\)  and FS image \(x_{f}\)  with the help of the context discriminator which can extract global and local features and classify it as real/fake. The context discriminator consists of three components: global feature extractor, local feature extractor and classifier. The global feature extractor (\(\Psi _{G}\) ) takes the entire image as input while the local feature extractor (\(\Psi _{L}\) ) takes the region of interest (ROI) (\(\Phi \) ) from the entire image. The classifier network (\(\Psi _{C}\) ) takes the concatenated feature vector (\(\Psi _{G}(x) || \Psi _{L}(x)\) ) to classify the input image as real/fake. The overview of the proposed architecture is shown in Figure REF . The joint optimization of the generator and context discriminator parameters is given by:
\(\min _{\theta _{G}} \max _{\theta _{D}} L_{Recon-GLGAN}(\theta _{D},\theta _{G}) = E_{x_{f} \sim p_{\text{train}}(x_{f})}[\log D_{\theta _{D}}(x_{f})] + \\ E_{x_{u} \sim p_{G}(x_{u})}[-\log ( D_{\theta _{D}}(G_{\theta _{G}}(x_{u})))]\) 
\(D_{\theta _{D}}(x) = \Psi _{C}(\Psi _{G}(x) || \Psi _{L}(\Phi (x)))\) 
